构建具有系统1和系统2融合的专业通用人工智能。

Towards Building Specialized Generalist AI with System 1 and System 2 Fusion

July 11, 2024

作者: Kaiyan Zhang, Biqing Qi, Bowen Zhou

cs.AI

摘要

在这篇观点论文中，我们介绍了专业通用人工智能（SGAI或简称SGI）的概念，这是通往人工通用智能（AGI）的关键里程碑。与直接扩展通用能力相比，SGI被定义为在至少一项任务上专门化的人工智能，超越人类专家，同时保留通用能力。这种融合路径使SGI能够迅速实现高价值领域。我们根据对专业技能掌握程度和通用性能的分类，将SGI分为三个阶段。此外，我们讨论了SGI在解决与大型语言模型相关的问题方面的必要性，如它们的通用性不足、专业能力、创新的不确定性和实际应用。此外，我们提出了一个用于发展SGI的概念框架，该框架整合了系统1和系统2认知处理的优势。该框架包括三个层次和四个关键组成部分，重点是增强个体能力并促进协作演化。最后，我们总结了潜在的挑战，并提出了未来的方向。我们希望所提出的SGI能够为进一步研究和实现AGI的应用提供启示。

English

In this perspective paper, we introduce the concept of Specialized Generalist Artificial Intelligence (SGAI or simply SGI) as a crucial milestone toward Artificial General Intelligence (AGI). Compared to directly scaling general abilities, SGI is defined as AI that specializes in at least one task, surpassing human experts, while also retaining general abilities. This fusion path enables SGI to rapidly achieve high-value areas. We categorize SGI into three stages based on the level of mastery over professional skills and generality performance. Additionally, we discuss the necessity of SGI in addressing issues associated with large language models, such as their insufficient generality, specialized capabilities, uncertainty in innovation, and practical applications. Furthermore, we propose a conceptual framework for developing SGI that integrates the strengths of Systems 1 and 2 cognitive processing. This framework comprises three layers and four key components, which focus on enhancing individual abilities and facilitating collaborative evolution. We conclude by summarizing the potential challenges and suggesting future directions. We hope that the proposed SGI will provide insights into further research and applications towards achieving AGI.